AI Agent Operational Lift for Born in New York, New York

Born Group is a digital transformation consultancy founded in 2001, headquartered in New York. With over 1,000 employees, Born operates at the intersection of business strategy, technology, and creative design. The firm helps clients—often large enterprises—navigate digital disruption by providing services in areas like customer experience (CX) design, enterprise technology implementation, data analytics, and product innovation. Born's model is project-based and client-centric, requiring deep industry knowledge, rapid synthesis of complex information, and the delivery of tailored, actionable solutions.

Why AI matters at this scale

For a mid-market professional services firm like Born, AI is not a luxury but a strategic imperative for scaling impact and maintaining competitiveness. At its size (1001-5000 employees), Born has the revenue base to fund meaningful AI experimentation but lacks the vast R&D budgets of global giants like Accenture or Deloitte. The sector—IT services—is itself being transformed by AI, with clients increasingly demanding AI-augmented solutions and partners who can demonstrate cutting-edge capabilities. Internally, AI offers a lever to improve the core economics of the business: enhancing the productivity of high-cost consultants, accelerating project lifecycles, and enabling the creation of new, high-value service offerings. Failure to adopt risks eroding margins, losing talent to more tech-forward competitors, and becoming irrelevant as client needs evolve.

Concrete AI opportunities with ROI framing

1. Augmented Research & Strategy Development: Born's consultants spend significant time researching markets, competitors, and technologies for client engagements. Deploying AI agents to automate data gathering, analysis, and initial insight generation can reduce this non-billable research time by an estimated 40-50%. This directly increases consultant capacity for higher-value strategic thinking and client interaction, improving project profitability and enabling the firm to take on more work without linearly increasing headcount.

2. Intelligent Proposal & Deliverable Automation: Responding to RFPs and creating client presentations is a repetitive but critical task. An LLM-powered system, fine-tuned on Born's past successful proposals, brand voice, and methodologies, can generate first drafts of documents, case studies, and slide decks. This can cut proposal creation time by up to 70%, dramatically improving business development efficiency and allowing teams to respond to more opportunities with higher-quality, more personalized submissions.

3. Predictive Project Analytics: Born manages hundreds of concurrent client projects with variable scopes and teams. A machine learning model analyzing historical project data (timelines, budgets, team composition, client satisfaction scores) can identify patterns leading to delays or cost overruns. By providing early warnings and recommending interventions, such a system could improve project margin predictability by 5-10% and significantly enhance client retention through more reliable delivery.

Deployment risks specific to this size band

Born's size presents unique adoption challenges. First, integration complexity: With an established workforce and likely heterogeneous tech stack (spanning CRM, project management, and collaboration tools), rolling out cohesive AI tools requires careful change management and technical integration to avoid creating siloed efficiencies. Second, talent & upskilling: Retraining over a thousand professionals, many of whom are not technologists, requires a substantial, well-structured program that minimizes downtime on billable client work. Third, client data security & IP: As a services firm, Born handles sensitive client information. Using third-party AI models or platforms introduces significant data governance, confidentiality, and compliance risks that must be meticulously managed to maintain trust. Finally, ROI measurement: For a services business, the ROI of AI must be traced to improved project margins, faster business development cycles, and new revenue streams, requiring new metrics and attribution models beyond traditional IT cost-saving measures.